An object of the invention is a method for image coding, applicable for compression of images and video sequences with noise character.
Contemporary techniques for lossy image and video sequences compression are known in the literature. See, e.g., Domanski “Obraz cyfrowy” Wydawnictwo Kominikacji i Lacznosci, edition 1, Warszawa 2010, from A. Bovik (editor); “Handbook of Image and Video Processing”, Academic Press Series in Communications, Networking, and Multimedia, San Diego, Calif.: Academic Press, A Harcourt Science and Technology Company, ISBN: 0-12-119790-5; and P. Symes “Digital Video Compression” ISBN-10: 0071424873, ISBN-13: 978-0071424875, 2003. Such techniques are also disclosed in the description of the H.264/AVC standard. See, e.g., “ISO/IEC 14496-10:2010. Information technology—Coding of audio-visual objects—Part 10: Advanced Video Coding”.
Spectrum information compression methods are also known in the literature: by using a DCT (discrete cosine transform), as in the JPEG standard, described in ISO/IEC IS 10918-1, ITU-T recommendation T.81; and by using filter parameters of finite and infinite impulse response, parametric coding with polynomial representation, and RC or LAR (log-area ratio) parameters as described in R. Viswanathan J. Makhoul, “Quantization Properties of Transmission Parameters in Linear Predictive Systems,” IEEE Trans. Acoust., Speech, and Audio Processing, vol. 23, pp. 309-321, 1975.
Noise reduction in video sequences is a highly developed field. The overview of noise reduction methods can be found in the literature. See, e.g., A. Bovik (editor) “Handbook of Image and Video Processing”, Academic Press Series in Communications, Networking and Multimedia, San Diego, Calif.: Academic Press, A Harcourt Science and Technology Company, ISBN: 0-12-119790-5.
Contemporary techniques for reduction of noise in video sequences are also known in the literature. See, e.g., J. Dai, O. C. Au, Ch. Pang. W. Yang, F. Zou, “Film grain noise removal and synthesis in video coding”, IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) 2010, pp. 890-893, 2010; M. O. Zaw, K. H. Goh, J. Y. Tham, W. S. Lee, “A low complexity texture-discriminating noise removal method for video encoding”, 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1701-1705, 2010; and V. Zlokolica, W. Philips, D. Van De Ville, “Robust non-linear filtering for video processing”, 14th International Conference on Digital Signal Processing (DSP) 2002. vol 2, pp. 571-574, 2002. The purpose of these techniques is to provide the recipient (the viewer) with a possibly high quality noise-free content. The target quality is determined objectively (in relation to its theoretical noise model), or subjectively (customer experience).
Synthetic noise images generation techniques are also known in the literature. See, e.g., R. Gonzales, R. Woods, “Digital Image Processing”, Addison Wesley, pp. 187-213, 1992; A. Jain “Fundamentals of Digital Image Processing”, Prentice Hall, pp. 244-253, 273-275, 1989; E. Davies “Machine Vision: Theory, Algorithms and Practicalities”, Academic Press, pp. 29-30, 40-47, 493, 1990; and B. Horn “Robot Vision|, MIT Press, vol. 2, 1986, and from A. Marion “An Introduction to Image Processing, Chapman and Hall”, chapter 5, 1991.
The image compression techniques presented by the literature do not exploit the method according to the present invention.